2. Research Question
Is the number of credit hours taken at
SLCC per week related to the
number of hours per week that the
student works at a job?
3. Approach:
• We are going to collect data by Systematic sampling and choose
every 10’th student walking by. We are going to sample on a
Tuesday and Wednesday of the same week. This should give us the
largest percentage of students taking classes. Wednesday students
will most likely be on a Monday/Wednesday schedule and the
Tuesday students should be on a Tuesday/Thursday schedule.
Each group member will survey for 2 hours per day with the first
member of the group starting at 7:45am to catch the 8:00 classes.
The next group member will take over at 9:45 and so on until the
5’th group member finishes at 6:00 pm. We will choose different
locations around the campuses to survey from so we get the widest
range of students in different fields of study.
Using systematic sampling and different locations around the
campuses should give us a very good random sample of students.
7. Results
Simple linear regression results:
Dependent Variable: Work Hours
Independent Variable: Credit Hours
Work Hours = 37.872406 - 0.98368984 Credit Hours
Sample size: 103
R (correlation coefficient) = -0.2587
R-sq = 0.06694811
Estimate of error standard deviation: 14.91806
Credit hours Working Hours
Mean 10.2 27.83
Standard Deviation 4.04 15.37
Range 15 60
Mode 12 40
Median 11 30
Mimimum 3 0
Maximum 18 60
Q1 6 20
Q3 13 40
8. Summary of Statistics
• As a group we decided to look at the relation between credit hours taken
at SLCC and hour per week worked. We surveyed students at different
times and at different SLCC campuses to try to get a good diversity of the
student population at SLCC. We found that there was a wide range of
credit hours and hours worked for students, range for credit hours was 3
to 18, and range for hours worked was 0 to 60. Our mean was 10.2 for
credit hours and 27.8 for working hours. Most of the students surveyed
were full time students with a mode of 12 credit hours. The mode for
hours worked was 40, which means most of the student had full time jobs
as well. The correlation coefficient was -0.26, telling us that there is a very
small negative correlation. Our sample size was 103; we suspect that if we
were able to survey more students, there would be a stronger negative
correlation. This would strengthen the argument that as the amount of
credit hours increases, the amount of working hours does decrease in a
negative correlation.
9. Term Project
Group I – Credits
•Jamie Wilkey:
Graphs, Research Question, Approach, Data,
Formatting and Clip art. Submission
•Kylie Bolton & Tori Ackerman:
Summary of Statistics
•Dale Gehrig:
Results
